Experimental Results of a Michigan-like Evolution Strategy for Non-stationary Clustering

Non-stationary clustering deals with the clustering of a sequence of data samples obtained at a diverse time instant. A paradigm case of non-stationary clustering is the color quantization of image sequences. We propose an efficient evolution strategy to compute adaptively the color representatives for each image in the sequence.

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